OBJECTIVES: The goal of this study was to determine the relation between EEG, event-related potentials and information processing as measured by an acoustical choice reaction time task. In particular, we wanted to find out to what extent reaction-time performance is related to the pre-stimulus EEG activity (frequency domain) and the magnitude of signal power as well as noise power (stimulus-uncorrelated activity) after the tones (time domain). MATERIALS AND METHODS: For parametrization, EEG-activity was factorized across pre-defined frequency bands and 19 electrode positions, applying spectral power and coherence analysis. Signal power was estimated by calculating the mean power of the evoked single sweeps. Noise power was computed by subtracting the latter minus the power of the average evoked potential. We investigated 254 healthy subjects who had to perform an acoustical choice reaction task during running EEG. RESULTS: In the frequency domain, it was found that high frontally pronounced delta-power in the pre-stimulus EEG correlates with fast reaction-time performance, which was regarded as the expression of a readiness potential in the frequency domain, reflecting increased cortical activation. In the time domain, fast reaction times were found to be correlated with the amplitude of the event-related potential N100 as well as with the signal power and signal-to-noise ratio of the evoked activity. This result pointed to the frequently described relation between evoked signals and information processing. In accordance with the theory of stochastic resonance, we also found a positive correlation between the magnitude of noise power after the stimulus and reaction-time performance. Besides, noise power was found to be positively correlated with pre-stimulus cortical activation (mainly in the delta and alphal frequency band), whereas no relation was found between pre-stimulus EEG and the signal power of the event-related activity, except for a weak relation to the alpha2 power. CONCLUSION: Our findings support the notion that information processing is not only dependent on signal strength but also on a certain amount of basic noise, reflecting the overall energy state of the brain.
OBJECTIVES: The goal of this study was to determine the relation between EEG, event-related potentials and information processing as measured by an acoustical choice reaction time task. In particular, we wanted to find out to what extent reaction-time performance is related to the pre-stimulus EEG activity (frequency domain) and the magnitude of signal power as well as noise power (stimulus-uncorrelated activity) after the tones (time domain). MATERIALS AND METHODS: For parametrization, EEG-activity was factorized across pre-defined frequency bands and 19 electrode positions, applying spectral power and coherence analysis. Signal power was estimated by calculating the mean power of the evoked single sweeps. Noise power was computed by subtracting the latter minus the power of the average evoked potential. We investigated 254 healthy subjects who had to perform an acoustical choice reaction task during running EEG. RESULTS: In the frequency domain, it was found that high frontally pronounced delta-power in the pre-stimulus EEG correlates with fast reaction-time performance, which was regarded as the expression of a readiness potential in the frequency domain, reflecting increased cortical activation. In the time domain, fast reaction times were found to be correlated with the amplitude of the event-related potential N100 as well as with the signal power and signal-to-noise ratio of the evoked activity. This result pointed to the frequently described relation between evoked signals and information processing. In accordance with the theory of stochastic resonance, we also found a positive correlation between the magnitude of noise power after the stimulus and reaction-time performance. Besides, noise power was found to be positively correlated with pre-stimulus cortical activation (mainly in the delta and alphal frequency band), whereas no relation was found between pre-stimulus EEG and the signal power of the event-related activity, except for a weak relation to the alpha2 power. CONCLUSION: Our findings support the notion that information processing is not only dependent on signal strength but also on a certain amount of basic noise, reflecting the overall energy state of the brain.
Authors: Georg Winterer; Frederick W Carver; Francesco Musso; Venkata Mattay; Daniel R Weinberger; Richard Coppola Journal: Hum Brain Mapp Date: 2007-09 Impact factor: 5.038
Authors: Wen-Liang Zhou; Shaina M Short; Matthew T Rich; Katerina D Oikonomou; Mandakini B Singh; Enas V Sterjanaj; Srdjan D Antic Journal: Neurophotonics Date: 2014-12-29 Impact factor: 3.593
Authors: Shaina M Short; Katerina D Oikonomou; Wen-Liang Zhou; Corey D Acker; Marko A Popovic; Dejan Zecevic; Srdjan D Antic Journal: J Neurophysiol Date: 2017-05-31 Impact factor: 2.714
Authors: Annemarie Wolff; Javier Gomez-Pilar; Jianfeng Zhang; Joelle Choueiry; Sara de la Salle; Verner Knott; Georg Northoff Journal: Cereb Cortex Date: 2022-08-03 Impact factor: 4.861